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Global estimates of undiagnosed diabetes in adults

Published:December 03, 2013DOI:https://doi.org/10.1016/j.diabres.2013.11.001

      Abstract

      Aims

      The prevalence of diabetes is rapidly increasing worldwide. Type 2 diabetes may remain undetected for many years, leading to severe complications and healthcare costs. This paper provides estimates of the prevalence of undiagnosed diabetes mellitus (UDM), using available data from high quality representative population-based sources.

      Methods

      Data sources reporting both diagnosed and previously undiagnosed diabetes were identified and selected according to previously described IDF methodology for diabetes in adults (aged 20–79). Countries were divided into 15 data regions based on their geographic IDF Region and World Bank income classification. The median UDM proportion was calculated from selected data sources for each of data region. The number of UDM cases in 2013 was calculated from country, age and sex-specific estimates of known diabetes cases and data region-specific UDM proportion.

      Results

      Of 744 reviewed data sources, 88 sources representing 74 countries had sufficient information and were selected for generation of estimates of UDM. Globally, 45.8%, or 174.8 million of all diabetes cases in adults are estimated to be undiagnosed, ranging from 24.1% to 75.1% across data regions. An estimated 83.8% of all cases of UDM are in low- and middle-income countries. At a country level, Pacific Island nations have the highest prevalence of UDM.

      Conclusions

      There is a high proportion of UDM globally, and especially in developing countries. Further high-quality studies of UDM are needed to strengthen future estimates.

      Keywords

      1. Introduction

      The number and prevalence of people with diabetes is rapidly increasing [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence in adults for 2013 and projections for 2035 for the IDF Diabetes Atlas.
      ]. The International Diabetes Federation (IDF) estimates that there are 381.8 million people with diabetes in 2013 with a projected increase of 55% to 591.9 million by 2035 [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence in adults for 2013 and projections for 2035 for the IDF Diabetes Atlas.
      ]. As a result of a combination a number of factors including: under-performing health systems, low awareness among the general public and health professionals, and the often slow onset of symptoms or progression of type 2 diabetes, the condition may remain undetected for many years, during which time complications may develop. Population-based studies actively screening for diabetes using either oral glucose tolerance test (OGTT) or fasting blood glucose provide the backbone for estimating undiagnosed diabetes (UDM). In such studies, participants who report not having been diagnosed with diabetes may be found to have diabetes upon testing of their blood glucose and would therefore be classified as having UDM, i.e., ‘previously undiagnosed’ or ‘newly diagnosed’ diabetes. While it is possible to have undiagnosed type 1 diabetes, this is usually short in duration due to the rapid onset of symptoms, and would not likely be measured in the population-based studies necessary for the estimation of undiagnosed diabetes. However, few studies reporting the prevalence of diabetes make a distinction between type 1 and type 2 diabetes, and it is therefore not possible to separate any estimate of undiagnosed diabetes.
      The prolonged asymptomatic phase of type 2 diabetes may last many years [
      American Diabetes Association
      Diagnosis and classification of diabetes mellitus.
      ], during which time unmanaged elevated blood glucose leads to serious and irreversible development of micro- and macrovascular complications including neuropathy, nephropathy, retinopathy, coronary artery disease, stroke and peripheral vascular disease [
      • Fowler M.J.
      Microvascular and macrovascular complications of diabetes.
      ,
      • Vinik A.
      • Flemmer M.
      Diabetes and macrovascular disease.
      ]. Rates of complications have been shown to be high in people with UDM compared to normoglycaemic individuals. In the USA, up to 41.7% of adults with previously undiagnosed diabetes have chronic kidney disease [
      • Plantinga L.C.
      • Crews D.C.
      • Coresh J.
      • Miller 3rd, E.R.
      • Saran R.
      • Yee J.
      • et al.
      Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or prediabetes.
      ]. The prevalence of some level of diabetic retinopathy among individuals with UDM in China is over 30% [
      • Hu Y.H.
      • Pan X.R.
      • Liu P.A.
      • Li G.W.
      • Howard B.V.
      • Bennett P.H.
      Coronary heart disease and diabetic retinopathy in newly diagnosed diabetes in Da Qing, China: the Da Qing IGT and Diabetes Study.
      ], and a recent review found the prevalence of diabetic retinopathy to exceed 15% in one third of all populations investigated [
      • Ruta L.M.
      • Magliano D.J.
      • Lemesurier R.
      • Taylor H.R.
      • Zimmet P.Z.
      • Shaw J.E.
      Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries.
      ]. Furthermore, BMI, blood pressure, and other cardiovascular and metabolic markers have been found to be significantly higher in a cohort of people with coronary artery disease and also UDM compared with diagnosed diabetes; likely due to awareness of the condition and subsequent dietary modifications [
      • Tenenbaum A.
      • Motro M.
      • Fisman E.Z.
      • Boyko V.
      • Mandelzweig L.
      • Reicher-Reiss H.
      • et al.
      Clinical impact of borderline and undiagnosed diabetes mellitus in patients with coronary artery disease.
      ]. Undiagnosed diabetes has been reported to carry a similar risk of mortality to diagnosed diabetes, and is associated with a 1.5- to 3.0-fold higher risk of mortality compared to normoglycaemic individuals [
      • Wild S.H.
      • Smith F.B.
      • Lee A.J.
      • Fowkes F.G.
      Criteria for previously undiagnosed diabetes and risk of mortality: 15-year follow-up of the Edinburgh Artery Study cohort.
      ,
      • Valdés S.
      • Botas P.
      • Delgado E.
      • Díaz Cadórniga F.
      Mortality risk in Spanish adults with diagnosed diabetes, undiagnosed diabetes or pre-diabetes. The Asturias study 1998–2004.
      ].
      Without the mechanisms and resources necessary for early detection, a person with diabetes may only be diagnosed after the onset of complications. Prevention of complications in people with diabetes by timely lifestyle and pharmaceutical interventions has been shown to reduce hyperglycaemia and risk of complications, [
      • Genuth S.
      • Eastman R.
      • Kahn R.
      • Klein R.
      • Lachin J.
      • Lebovitz H.
      • et al.
      Implications of the United Kingdom Prospective Diabetes Study.
      ,
      • Holman R.R.
      • Paul S.K.
      • Bethel M.A.
      • Matthews D.R.
      • Neil H.A.W.
      10-year follow-up of intensive glucose control in type 2 diabetes.
      ,
      • Gaede P.
      • Lund-Andersen H.
      • Parving H.-H.
      • Pedersen O.
      Effect of a multifactorial intervention on mortality in type 2 diabetes.
      ] but this potential benefit is lost in people with UDM [
      • Engelgau M.M.
      • Narayan K.M.
      • Herman W.H.
      Screening for type 2 diabetes.
      ]. In addition to a heavy health burden, the financial costs of diabetes-related health expenditures weigh heavily on individuals, health systems and governments, with global health expenditure estimated to be at least 548.5 billion USD in 2013 [

      IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013.

      ]. The cost of undiagnosed diabetes may contribute substantially to this estimate. One study from the USA found that an additional 2864 USD were spent on direct and indirect costs per person with UDM per year, or 18 billion USD nationally [
      • Zhang Y.
      • Dall T.M.
      • Mann S.E.
      • Chen Y.
      • Martin J.
      • Moore V.
      • et al.
      The economic costs of undiagnosed diabetes.
      ]. While the cost of screening for and subsequently treating diabetes is considerable, it is far outweighed by the cost of treating potentially preventable diabetes-related complications [
      • Herman W.H.
      • Eastman R.C.
      The effects of treatment on the direct costs of diabetes.
      ,
      • O’Brien J.A.
      • Patrick A.R.
      • Caro J.
      Estimates of direct medical costs for microvascular and macrovascular complications resulting from type 2 diabetes mellitus in the United States in 2000.
      ]. It is important to produce regional and global estimates of UDM in order to understand the burden of UDM globally and regionally, its drivers and potential implications for policy and practice.
      While the existence of UDM has long been recognised [
      • King H.
      • Rewers M.
      Global estimates for prevalence of diabetes mellitus and impaired glucose tolerance in adults.
      ], wide-reaching awareness among the general public, physicians and policy-makers is lacking and there are limited reliable and comparable data available on the subject. Nationally representative population-based studies using OGTT are considered the gold standard for studying the prevalence of diabetes and quantifying undiagnosed diabetes [
      • King H.
      • Rewers M.
      Global estimates for prevalence of diabetes mellitus and impaired glucose tolerance in adults.
      ]. However, the availability of these studies is varied and may be limited for similar reasons to those which cause diabetes to go undiagnosed; namely that screening effectively for diabetes is costly, time consuming, and, for many countries, not a priority. However these considerations should be balanced by the costs and health burden associated with UDM.
      IDF first produced estimates of UDM in 2011 [

      IDF Diabetes Atlas. 5th ed. Brussels, Belgium: International Diabetes Federation; 2011.

      ], providing a global-scale quantification of this burden. An accurate estimation of the burden of UDM is highly relevant given the high health-related and financial costs associated with diabetes.
      Given the lack of awareness and considerable burden of UDM, this paper presents a standardised method and accompanying results for country, regional and global estimates of UDM for the year 2013. These estimates are included in the 6th edition of the IDF Diabetes Atlas [

      IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013.

      ].

      2. Methods

      2.1 Literature review and selection of data sources

      The IDF methodology for estimating diabetes prevalence has previously been described and updated [
      • Guariguata L.
      • Whiting D.R.
      • Beagley J.
      • Linnenkamp U.
      • Hambleton I.
      • Cho N.H.
      • et al.
      Global estimates of diabetes prevalence in adults for 2013 and projections for 2035 for the IDF Diabetes Atlas.
      ,
      • Guariguata L.
      • Whiting D.
      • Weil C.
      • Unwin N.
      The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults.
      ] and is summarised in Fig. 1. Briefly, data sources reporting the prevalence of diabetes were identified through a systematic literature search for the period November 2010–June 2013, using PubMed, Google Scholar, websites of governments, the World Health Organization and associated organisations, personal communication with investigators in the IDF network, and by searching reference lists.
      Figure thumbnail gr1
      Fig. 1Study and data source selection and generation of estimates for undiagnosed diabetes mellitus (UDM) in adults (20–79 years), 2013.
      Data from 744 retrieved data sources were stored in database MySQL database and characterised by the following criteria: type of data (e.g. peer-reviewed publication), study design (e.g. population-based), sample representation (e.g. regional representation), diagnostic criteria (e.g. oral glucose tolerance test – OGTT), sample size, and study year. Data sources lacking sufficient data on age-specific prevalence of diabetes, essential information on methodology, or details on study characteristics were excluded, and also duplicate or out-dated data, and data from clinic- or hospital-based studies. The study characteristics were then used to score each data source using a scoring system based on the Analytic Hierarchy Process [
      • Saaty T.L.
      Decision making with the analytic hierarchy process.
      ] derived from the collective opinion of an expert panel as previously described [
      • Guariguata L.
      • Whiting D.
      • Weil C.
      • Unwin N.
      The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults.
      ]. Data sources that were nationally representative, population-based, used oral glucose tolerance test (OGTT), and were conducted in the last five years were favoured. Sources including data on both self-reported (i.e., diagnosed prior to the study) and previously undiagnosed (i.e., diabetes first diagnosed during the study) were used for the estimation of UDM.

      2.2 Aggregation and calculation of proportion of UDM

      The proportion of UDM (i.e. percentage of all cases of diabetes that are undiagnosed) was extracted or calculated from data sources reporting both diagnosed and previously undiagnosed diabetes. Due to limited data availability, countries were grouped into 15 data regions grouped by a combination of the seven IDF Regions (Africa – AFR; Europe – EUR; Middle East and North Africa – MENA; North America and the Caribbean – NAC; South and Central America – SACA; South East Asia – SEA; and Western Pacific – WP) and World Bank Income classification group (high, middle and low income countries; HIC, MIC and LIC, respectively [

      The World Bank. How we Classify Countries [Internet]. [cited 2013 April 15]. Available from: http://data.worldbank.org/about/country-classifications.

      ]) as reported in April 2013 [

      The World Bank. GNI per capita (current US$) [Internet]. [cited 2013 April 15]. Available from: http://data.worldbank.org/indicator/NY.GNP.PCAP.CD.

      ] to produce a combined category, or “data region” (e.g. AFR-MIC). No data sources were available for MENA-LIC or SACA-HIC. Therefore, countries which were in the data regions MENA-LIC (namely, Afghanistan) and SACA-HIC (namely, Puerto Rico) were grouped with MENA-MIC and NAC-HIC, respectively. Sets of source data on the proportion of UDM were grouped by data region and the median was calculated to arrive at the estimated proportion of UDM for each data region.

      2.3 Estimates of cases and prevalence of undiagnosed diabetes

      The total number of cases in adults (aged 20–79) for 219 countries and territories were calculated using the estimated UDM proportion matched by data region and the country-, sex-, and age-specific estimates of the number of adults with diabetes. Cases of UDM were aggregated to produce global and regional estimates of UDM, as well as estimates by income group. The prevalence of UDM (i.e. the percentage of the population with UDM) was calculated by dividing the age-specific number of cases of UDM in adults by the age-specific adult (20–79 years) population [

      United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2012 Revision [Internet]. New York; 2013. Available from: http://esa.un.org/wpp/Documentation/publications.htm.

      ] to produce estimates of the prevalence of UDM.

      3. Results

      3.1 Literature search

      Of the 744 reviewed data sources, 174 were used for diabetes prevalence estimates in adults (20–79 years); 88 sources with information on known and previously undiagnosed diabetes were selected (Fig. 1, Appendix).
      Table 1 presents the UDM proportion by data region, including study characteristics and range of data that contributed to the regional estimate. Overall, data were drawn from 74 of 219 countries (33.8%). Middle-income countries had the highest proportion of countries represented by original source data (39.4%; n = 43) compared to HICs (28.4%; n = 19) and LICs (27.9% n = 12) countries. By IDF Region, SEA had the highest proportion of countries with selected data (71.4%) followed by SACA (55.0%), and MENA (50.0%). Only 25.0% of countries in AFR had data included in the estimates. Just over half of all 88 selected data sources were based on OGTT (54.2% in HICs, 53.8% of studies in MICs, and 33.3% in LICs, respectively), and 75% of all data sources were nationally representative (95.8% of studies in HICs, 67.3% in MICs and 66.7% in LICs, respectively).
      Table 1Study sources, characteristics by data region and proportion of undiagnosed diabetes (UDM) in adults (20–79 years), 2013.
      Data regionNo. of studiesCountries in data region providing dataCountries (no. of studies)Study sample representationStudy diagnosticStudy range DM prevalence (%)Study range UDM proportion (%)Data region UDM proportion (%)
      AFR-MIC54/17; 23.5%Angola (1); Réunion (1); Seychelles (1); South Africa (2)es (1); nat (2); reg (2)OGTT (5)2.8–20.135.5–91.746.0
      AFR-LIC88/31; 25.8%Benin (1); Comoros (1); Guinea (1); Kenya (1); Mozambique (1); Niger (1); Togo (1); United Republic of Tanzania (1)nat (7); reg (1)FBG (6); OGTT (2)2.6–10.536–99.175.1
      EUR-HIC1110/33; 30.3%Croatia (1); Cyprus (1); Estonia (1); Finland (1); France (2); Hungary (1); Malta (1); Portugal (1); Spain (1); United Kingdom (1)loc (1); nat (10)FBG (5); OGTT (6)5.1–14.616.7–60.936.6
      EUR-MIC44/20; 20.0%Albania (1); Bulgaria (1); Poland (1); Turkey (1)nat (3); reg (1)FBG (2); OGTT (2)4.2–16.527.7–45.535.1
      EUR-LIC11/3; 33.3%Uzbekistan (1)reg (1)OGTT (1)8.229.329.3
      MENA-HIC53/6; 50.0%Oman (2); Saudi Arabia (2); United Arab Emirates (1)nat (5)FBG (3); OGTT (2)10.2–23.727.9–5040.7
      MENA-MIC128/15; 53.3%Algeria (2); Egypt (1); Iraq (1); Islamic Republic of Iran (1); Jordan (3); Pakistan (1); State of Palestine (2); Tunisia (1)nat (11); reg (1)FBG (7); OGTT (5)5.9–25.921.7–7550.0
      NAC-HIC33/14; 21.4%Barbados (1); United States of America (1); US Virgin Islands (1)nat (3)HbA1c (1); OGTT (2)13.7–17.510.2–39.827.7
      NAC-MIC33/12; 25.0%Belize (1); Guadeloupe (1); Jamaica (1)nat (3)FBG (2); OGTT (1)6.6–13.118.5–41.225.0
      NAC-LIC11/1; 100%Haiti (1)reg (1)OGTT (1)11.329.429.4
      SACA-MIC1111/19; 57.9%Argentina (1); Bolivia (1); Chile (1); Colombia (1); Costa Rica (1); Ecuador (1); Guatemala (1); Honduras (1); Nicaragua (1); Peru (1); Venezuela (1)loc (2); nat (1); reg (8)FBG (6); OGTT (5)4.4–9.420.0–48.824.1
      SEA-MIC54/5; 80.0%Bhutan (1); India (1); Mauritius (2); Sri Lanka (1)loc (1); nat (4)OGTT (5)8.2–17.935.8–69.549.1
      SEA-LIC11/2; 50.0%Nepal (1)reg (1)FBG (1)14.643.643.6
      WP-HIC53/13; 23.1%Hong Kong SAR (1); Republic of Korea (2); Singapore (2)nat (5)FBG (2); OGTT (3)7.6–9.531.9–64.449.4
      WP-MIC129/21; 42.9%China (2); Fiji (1); Indonesia (1); Malaysia (3); Mongolia (1); Nauru (1); Samoa (1); Thailand (1); Tonga (1)nat (11); reg (1)FBG (7); OGTT (5)5.7–16.027.4–78.554.1
      WP-LIC11/5; 20.0%Cambodia (1)nat (1)FBG (1)2.96363.0
      Es, ethnic or specific group; loc, local; nat, national; reg, regional; FBG, fasting blood glucose; OGTT, oral glucose tolerance test.

      3.2 Estimates of UDM

      Globally, 45.8% of all cases, or 174.8 million people, are estimated to have UDM in 2013. There is considerable variability in the UDM proportion across different regions, ranging from 24.1% in SACA-MICs to 75.1% in AFR-LICs (Table 1). Disaggregation of MICs to examine upper middle income countries (UMICs) and lower middle income countries (LMICs) alongside HICs and LICs shows that LMICs have the highest number of cases of UDM of all income groups, with 108.4 million. Overall, 83.8% of all cases of UDM are in LICs and MICs (Fig. 2). At the country level, Tokelau is estimated to have the highest prevalence of UDM, at 20.5%, followed by the Marshall Islands at 18.9% and the Federated States of Micronesia at 16.1%, whilst Azerbaijan has the lowest at 0.8% (Table 2). The 10 countries with the highest UDM prevalence are all Pacific Islands.
      Figure thumbnail gr2
      Fig. 2Total number of cases (1000s) of diabetes, both undiagnosed (UDM) and diagnosed, in adults (20–79 years), 2013, by World Bank income group [

      The World Bank. How we Classify Countries [Internet]. [cited 2013 April 15]. Available from: http://data.worldbank.org/about/country-classifications.

      ,

      The World Bank. GNI per capita (current US$) [Internet]. [cited 2013 April 15]. Available from: http://data.worldbank.org/indicator/NY.GNP.PCAP.CD.

      ] (HIC – high-income countries; UMIC – upper-middle-income countries; LMIC – lower-middle-income countries; LIC – low-income countries).
      Table 2Countries/territories with highest and lowest prevalence (%) and number of cases (1000s) of undiagnosed diabetes mellitus (UDM) in adults (20–79 years), 2013.
      Highest UDM prevalenceHighest UDM cases
      Country/territoryUDM prevalence (%)Country/territoryUDM cases (1000s)
      1Tokelau20.5China53 238.4
      2Marshall Islands18.9India31 920.0
      3Federated States of Micronesia16.1United States of America6761.7
      4Kiribati14.0Indonesia4627.8
      5Nauru12.6Russian Federation3830.0
      6Cook Islands12.5Egypt3755.3
      7Vanuatu11.4Japan3558.7
      8French Polynesia11.2Pakistan3356.3
      9New Caledonia10.2Brazil2870.0
      10Guam10.1Germany2766.1

      4. Discussion

      Globally, 174.8 million people are estimated to have UDM using the standardised IDF methodology described in this paper. These estimates confirm that lack of detection of diabetes persists throughout the world, across all regions and income groups. Despite a variation in availability and quality of data, studies were available for every IDF Region and all income groups (Table 1).
      While there is wide variability in the proportions of UDM from the underlying sources for the AFR-LIC region (Table 1) with a study from Togo reporting a proportion of 99.1% [
      • Agoudavi K.
      • Ministry of Health
      • et al.
      Togo STEPS Noncommunicable Disease Risk Factors Survey 2010 [Internet].
      ], as a whole, this data region has the highest proportion of UDM (75.1%). Not coincidentally, these countries represent some of the least developed health systems in the world and have been focused on a large infectious disease burden which likely contributes to a low awareness of NCDs and diabetes. Although the next highest proportion of UDM is found in WP-LIC countries, it is the middle-income countries in that region which contribute a large number of cases to the UDM burden. The WP-MIC statistic is based largely on estimates for China [
      • Yang W.
      • Lu J.
      • Weng J.
      • Jia W.
      • Ji L.
      • Xiao J.
      • et al.
      Prevalence of diabetes among men and women in China.
      ,
      • Li R.
      • Lu W.
      • Jiang Q.W.
      • Li Y.Y.
      • Zhao G.M.
      • Shi L.
      • et al.
      Increasing prevalence of type 2 diabetes in Chinese adults in Shanghai.
      ] with an estimated 54.1% of diabetes cases undiagnosed. However, new evidence shows that this may be an underestimate as a recent nationally representative survey of Chinese adults reported a proportion of UDM of 70% [
      • Xu W.
      • Xu Z.
      • Jia J.
      • Xie Y.
      • Wang H.-X.
      • Qi X.
      Detection of prediabetes and undiagnosed type 2 diabetes: a large population-based study.
      ].
      NAC-MIC and SACA-MIC have very similar UDM proportions (25.0% and 24.1%, respectively), reflecting the comparable states of health system development and state of the diabetes epidemic in those regions. Conversely, the estimate for NAC-LIC, which includes only Haiti, is based on a study from the capital city and is likely an underestimate of the national proportion of UDM which would include rural areas with a lower access to healthcare and likely poorer awareness of diabetes. For example, a study from Guinea, a country with a similar level of development to Haiti, reported a UDM proportion of 100% in the rural population, compared to 58.8% in the urban population [
      • Baldé N.-M.
      • Diallo I.
      • Baldé M.-D.
      • Barry I.-S.
      • Kaba L.
      • Diallo M.-M.
      • et al.
      Diabetes and impaired fasting glucose in rural and urban populations in Futa Jallon (Guinea): prevalence and associated risk factors.
      ]. The greatest range of data comes from AFR-LIC (36.0–99.1% across 8 studies), reflecting the broad range of GNI per capita within the LIC income group. Where more studies are available, the estimates may benefit from a narrower grouping of countries matched more closely for health system development.
      The countries with the highest prevalence and number of cases of UDM (Table 2) closely follow those for total diabetes. Seven of the top 10 countries for prevalence of UDM also feature in the top 10 countries for total diabetes prevalence, while this applies to 9 of the top 10 countries for cases. LMICs have by far the greatest number of cases of UDM, followed by HICs (Fig. 2). There is a complex interplay of factors contributing to UDM, rather than UDM being directly related to income. These determinants may include established risk factors associated with diabetes, but also awareness of the condition and access to and quality of healthcare [
      • Zhang X.
      • Geiss L.S.
      • Cheng Y.J.
      • Beckles G.L.
      • Gregg E.W.
      • Kahn H.S.
      The missed patient with diabetes: how access to health care affects the detection of diabetes.
      ]. Health systems are generally more developed in HICs (in terms of number, education, and resources available to health professionals) and thus diabetes may be detected earlier than in less developed regions where more barriers exist. In addition healthcare access may be lower in rural areas within countries in any income region [
      • Chan L.
      • Hart L.G.
      • Goodman D.C.
      Geographic access to health care for rural medicare beneficiaries.
      ]. Furthermore, living in poverty may be associated with a lesser degree of health-seeking behaviour [
      • Ahmed S.M.
      • Tomson G.
      • Petzold M.
      • Kabir Z.N.
      Socioeconomic status overrides age and gender in determining health-seeking behaviour in rural Bangladesh.
      ]. These factors exert varying degrees of influence in different countries within the income regions concerned, but may account for the variation. The overarching truth is that UDM will continue to be a public health issue even as economic development continues, unless commitments are made to strengthening health systems, with a particular focus on resource-poor regions.
      Data availability for the estimates varied considerably by region and especially by income group. Globally, 33.8% of countries from all seven IDF Regions were represented (Table 1). A higher proportion of MICs (39.4%) was represented than HICs and LICs (28.4% and 27.9%, respectively). High-income countries had by far the highest proportion of nationally representative data sources; however, there are a greater proportion of large nationally representative studies based on self-reported data in HICs with less focus on OGTT-based screening compared to MICs. This further illustrates that availability of resources does not necessarily correlate with a wealth of usable data for UDM.

      5. Limitations

      The principal limitation in generating accurate estimates for UDM is the lack of high quality data suitable for inclusion. Given that the data regions with sparse data sources tend to be those comprised of LICs and MICs, consideration should be given to how these countries could be supported. Many countries invest in large scale studies, but base these on self-reported data, or may either not record whether subjects had previously been diagnosed with diabetes before screening, or not report these data. A standardised approach to conducting studies and presenting results, with additional recommendations for developed settings would be beneficial. Issues pertaining to variability in methodology and suitable standardisation methods could also be resolved by a set of stepped study guideline recommendations.
      While the regional and global estimates in this report accurately reflect the available data, there is considerable intra-region and intra-country variation which is not reflected. The number of data sources is further limited by the selection criteria: while 292 of the 744 sets of data in the database contained data on UDM, only 88 (those which were also selected for generating estimates of diabetes prevalence) were used to generate estimates for UDM. Future estimates may benefit from a separate set of selection criteria for papers used to generate the estimates for UDM independent of the criteria for selection for overall prevalence estimates. Sensitivity analyses are underway to determine the effect of modifying the inclusion criteria for UDM on the global prevalence estimates.
      A further limitation is the lack of information on age-specific proportions of UDM. Evidence from six data sources (Fig. 3) show that proportions across different regions tend to be higher for younger age groups and decrease with age [
      • Solet J.-L.
      • Baroux N.
      • Pochet M.
      • Benoit-Cattin T.
      • De Montera A.-M.
      • Sissoko D.
      • et al.
      Prevalence of type 2 diabetes and other cardiovascular risk factors in Mayotte in 2008: the MAYDIA study.
      ,
      • Gardete-Correia L.
      • Boavida J.M.
      • Raposo J.F.
      • Mesquita A.C.
      • Fona C.
      • Carvalho R.
      • et al.
      First diabetes prevalence study in Portugal: PREVADIAB study.
      ,
      • Al Riyami A.
      • Elaty M.A.A.
      • Morsi M.
      • Al Kharusi H.
      • Al Shukaily W.
      • Jaju S.
      Oman world health survey: part 1 – methodology, sociodemographic profile and epidemiology of non-communicable diseases in Oman.
      ,
      • Costagliola D.
      • Delaunay C.
      • Moutet J.P.
      • Kankambega P.
      • Demeulemeester R.
      • Donnet J.P.
      • et al.
      The prevalence of diabetes mellitus in the adult population of Guadeloupe as estimated by history or fasting hyperglycemia.
      ,
      • Katulanda P.
      • Constantine G.R.
      • Mahesh J.G.
      • Sheriff R.
      • Seneviratne R.D.A.
      • Wijeratne S.
      • et al.
      Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka – Sri Lanka Diabetes, Cardiovascular Study (SLDCS).
      ,
      • Colagiuri S.
      • Colagiuri R.
      • Na’ati S.
      • Muimuiheata S.
      • Hussain Z.
      • Palu T.
      The prevalence of diabetes in the kingdom of Tonga.
      ]. The cause of the higher UDM proportions in younger age groups is likely two-fold. Firstly, routine screening tends to target older individuals, for example in the USA [
      American Diabetes Association
      Screening for diabetes.
      ], resulting in a much higher proportion of younger individuals who are undiagnosed. In addition, older individuals are more likely to have developed complications, leading to diagnosis, thus reducing UDM proportion in older age groups. Currently, the estimates only use a total proportion of UDM applied across all age groups and this may underestimate the proportion for younger age groups and over estimate for older ones.
      Figure thumbnail gr3
      Fig. 3Age-specific proportions (%) of undiagnosed diabetes mellitus (UDM) in adults (20–79 years), 2013. Age-specific UDM proportions were given only in a small number of data sources. One set of age-specific data from each IDF Region is presented [
      • Solet J.-L.
      • Baroux N.
      • Pochet M.
      • Benoit-Cattin T.
      • De Montera A.-M.
      • Sissoko D.
      • et al.
      Prevalence of type 2 diabetes and other cardiovascular risk factors in Mayotte in 2008: the MAYDIA study.
      ,
      • Gardete-Correia L.
      • Boavida J.M.
      • Raposo J.F.
      • Mesquita A.C.
      • Fona C.
      • Carvalho R.
      • et al.
      First diabetes prevalence study in Portugal: PREVADIAB study.
      ,
      • Al Riyami A.
      • Elaty M.A.A.
      • Morsi M.
      • Al Kharusi H.
      • Al Shukaily W.
      • Jaju S.
      Oman world health survey: part 1 – methodology, sociodemographic profile and epidemiology of non-communicable diseases in Oman.
      ,
      • Costagliola D.
      • Delaunay C.
      • Moutet J.P.
      • Kankambega P.
      • Demeulemeester R.
      • Donnet J.P.
      • et al.
      The prevalence of diabetes mellitus in the adult population of Guadeloupe as estimated by history or fasting hyperglycemia.
      ,
      • Katulanda P.
      • Constantine G.R.
      • Mahesh J.G.
      • Sheriff R.
      • Seneviratne R.D.A.
      • Wijeratne S.
      • et al.
      Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka – Sri Lanka Diabetes, Cardiovascular Study (SLDCS).
      ,
      • Colagiuri S.
      • Colagiuri R.
      • Na’ati S.
      • Muimuiheata S.
      • Hussain Z.
      • Palu T.
      The prevalence of diabetes in the kingdom of Tonga.
      ]. No age-specific data was found for SACA.
      There is also evidence from nationally representative studies of variation in the proportion of UDM between urban and rural settings. More studies reporting nationally representative figures that take this variation into account would benefit the precision of these estimates.

      6. Conclusion

      The results of this global study confirm the alarmingly high proportions of UDM in many areas of the world. Undiagnosed diabetes is harmful and costly; both financially and in terms of complications for individuals, communities, and health systems. Nonetheless, it is imperative that the response to these data should be appropriate to the varying capacities of national health systems. Even in countries with the most developed health systems, the proportion of patients not achieving target measurements are disturbingly high; in the UK, over 35% of patients with diabetes do not attain their target HbA1c, and almost 50% do not reach their target blood pressure [
      • Diabetes UK
      State of the Nation: England.
      ]. The provision of effective care to people with diabetes must be the first priority, before considering strategies to improve screening for UDM and thus identifying more people with diabetes.
      The finding that almost half of all diabetes still remains undiagnosed suggests that the current levels of awareness are alarmingly low. Lack of diagnosis of diabetes is a grave burden on health and financial systems throughout the world, and proportions are highest in developing countries. Addressing this challenge will require gradual and careful work, beginning with the strengthening of health systems in preparation for effectively accommodating and treating a higher number of people with diabetes diagnosed by subsequent screening programmes. In addition, more high-quality studies are needed to further understand the burden of UDM and allocate appropriate resources to closing the gap in diabetes care, and the authors hope that the transparency of this methodology will encourage the involvement of the research community.

      Conflict of interest

      The authors declare that they have no potential conflict of interest, including specific financial interests, relevant to the subject of this manuscript.

      Funding

      The 6th edition of the IDF Diabetes Atlas was supported by the following sponsors: Lilly Diabetes , Merck and Co, Inc. , Novo Nordisk A/S supported through an unrestricted grant by the Novo Nordisk Changing Diabetes® initiative, Pfizer, Inc. , and Sanofi Diabetes .

      Acknowledgements

      With many thanks to Dr Lydia Makaroff for carefully reviewing this manuscript, and to the IDF Diabetes Atlas Committee, and in particular Dr David Whiting, Professor Jonathan Shaw and Professor Ian Hambleton for their assistance and expertise in developing the estimates.

      Appendix A. Supplementary data

      Appendix.

      Tabled 1
      Data regionCountryCitationStudy yearSample sizeDiagnosticPublication typeRepresentationUDM proportion
      AFR-MICAngolaDiabetol Metab Syndr. 2010 Nov 1;2:632010421OGTTPeer-reviewedRegional91.67
      AFR-MICRéunionDiabetes Res Clin Pract. 2005 Mar;67(3):234–4220013600OGTTPeer-reviewedNational35.51
      AFR-MICSeychellesBMC Public Health. 2007 Jul 19;7:16320041255OGTTPeer-reviewedNational46
      AFR-MICSouth AfricaDiabetes Care. 2008 Sep;31(9):1783–820081025OGTTPeer-reviewedRegional84.8
      AFR-MICSouth AfricaPLoS One. 2012;7(9):e4333620081099OGTTPeer-reviewedEthnic or specific group40.4
      AFR-LICBeninWHO STEPS Report Benin, 200820083822FBGNational Health SurveyNational73.27
      AFR-LICComorosDiabetes Metab. 2010 Dec 27. [Epub ahead of print]20081268OGTTPeer-reviewedNational51.43
      AFR-LICGuineaDiabetes Metab. 2007 Apr;33(2):114–20. Epub 2007 Mar 2320071535FBGPeer-reviewedRegional70.33
      AFR-LICKenyaDiabetes Res Clin Pract. 2009 Jun;84(3):303–10. Epub 2009 Apr 920081459OGTTPeer-reviewedNational36
      AFR-LICMozambiqueDiabetes Metab. 2011 Jan 12. [Epub ahead of print]20052343FBGPeer-reviewedNational86.7
      AFR-LICNigerWHO STEPS Report Niger, 200720072722FBGNational Health SurveyNational99.15
      AFR-LICTogoWHO STEPS Togo, 201020103698FBGNational Health SurveyNational92
      AFR-LICUnited Republic of TanzaniaWHO STEPS Tanzania, 201220114867FBGNational Health SurveyNational76.9
      EUR-HICCroatiaDiabetes Res Clin Pract. 2008 Aug;81(2):263–7. Epub 2008 Jun 519971653FBGPeer-reviewedNational42
      EUR-HICCyprusDiabetes Care. 2006 Jul;29(7):1714–520051200OGTTPeer-reviewedNational36.59
      EUR-HICEstoniaDiabet Med. 2011. 28;504–5052008495OGTTPeer-reviewedLocal48.72
      EUR-HICFinlandBMC Public Health. 2008 Dec 29;8:42320052825OGTTPeer-reviewedNational60.94
      EUR-HICFranceDiabetes Metab. 2001 Jun;27(3):347–5819973508FBGPeer-reviewedNational50.24
      EUR-HICFranceDiabet Med. 2011 Feb 5. doi: 10.1111/j.1464-5491.2011.03250.x20072012FBGPeer-reviewedNational25
      EUR-HICHungaryCroat Med J. 2010 Apr;51(2):151–620061803FBGPeer-reviewedNational16.67
      EUR-HICMaltaDiabetes Res Clin Pract. 1989 Jun 20;7(1):7–1619811422OGTTPeer-reviewedNational31.42
      EUR-HICPortugalDiabet Med. 2010 Aug;27(8):879–8120105167OGTTPeer-reviewedNational43.59
      EUR-HICSpainDiabetologia. 2012 Jan;55(1):88–93. doi: 10.1007/s00125-011-2336–9. Epub 2011 Oct 1120105072OGTTPeer-reviewedNational24.2
      EUR-HICUnited KingdomDiabet Med. 2009 Jul;26(7):679–8520056739FBGPeer-reviewedNational18.5
      EUR-MICAlbaniaRural Remote Health. 2007 Apr–Jun;7(2):744. Epub 2007 Jun 2520063709FBGPeer-reviewedRegional30.32
      EUR-MICBulgariaEndocrinologia. 2007. 7(1);42–4920042055OGTTPeer-reviewedNational39.8
      EUR-MICPolandPol Arch Med Wewn. 2011 May;121(5):156–63200514 769FBGPeer-reviewedNational27.7
      EUR-MICTurkeyEur J Epidemiol. 2013 Feb;28(2):169–80. doi: 10.1007/s10654-013-9771–5201026 499OGTTPeer-reviewedNational45.5
      EUR-LICUzbekistanDiabet Med. 1998 Dec;15(12):1052–6219971956OGTTPeer-reviewedRegional29.34
      MENA-HICOmanSultanate of Oman National Health Survey, 200020005840FBGNational Health SurveyNational37.07
      MENA-HICOmanOman Med J. 2012 Sep;27(5):425–4320085000FBGPeer-reviewedNational50
      MENA-HICSaudi ArabiaDiabet Med. 1997 Jul;14(7):595–602199313 177OGTTPeer-reviewedNational48.5
      MENA-HICSaudi ArabiaSaudi Med J. 2004 Nov;25(11):1603–10200016 917FBGPeer-reviewedNational27.87
      MENA-HICUnited Arab EmiratesDiabetes Res Clin Pract. 2005 Aug;69(2):188–9520005839OGTTPeer-reviewedNational40.7
      MENA-MICAlgeriaDiabetes Metab. 2001 Apr;27(2 Pt 1):164–7119981457OGTTPeer-reviewedRegional50
      MENA-MICAlgeriaWHO STEPS Algeria, 200520034000FBGNational Health SurveyNational65.75
      MENA-MICEgyptWHO STEPS Egypt, 200620059780FBGNational Health SurveyNational62.02
      MENA-MICIraqWHO STEPS Iraq, 200620064503FBGNational Health SurveyNational47.05
      MENA-MICIslamic Republic of IranBMC Public Health. 2009 May 29;9:16720074233FBGPeer-reviewedNational47.1
      MENA-MICJordanJ Intern Med. 1998 Oct;244(4):317–2319962836OGTTPeer-reviewedNational50
      MENA-MICJordanPrev Chronic Dis. 2008 Jan;5(1):A17. Epub 2007 Dec 152004710FBGPeer-reviewedNational65.25
      MENA-MICJordanPrev Chronic Dis. 2012;9:E25. Epub 2011 Dec 1520073654FBGPeer-reviewedNational23.08
      MENA-MICPakistanDiabetes Res Clin Pract. 2007 May;76(2):219–22. Epub 2006 Sep 2619995433OGTTPeer-reviewedNational61.47
      MENA-MICState of PalestineEast Mediterr Health J. 2001 Jan–Mar;7(1–2):67–781998492OGTTPeer-reviewedNational21.67
      MENA-MICState of PalestineEast Mediterr Health J. 2000 Sep–Nov;6(5–6):1039–451996500OGTTPeer-reviewedNational28.57
      MENA-MICTunisiaEur J Clin Nutr. 2007 Feb;61(2):160–5. Epub 2006 Aug 919973729FBGPeer-reviewedNational75
      NAC-HICBarbadosInt J Epidemiol. 2002 Feb;31(1):234–919924709HbA1cPeer-reviewedNational10.2
      NAC-HICUnited States of AmericaNational Health and Nutrition Examination Survey, 20112008597OGTTNational Health SurveyNational39.78
      NAC-HICUS Virgin IslandsJ Natl Med Assoc. 2002 Mar;94(3):135–4219951026OGTTPeer-reviewedNational27.71
      NAC-MICBelizeCAMDI Report Belize, 200920092441OGTTNational Health SurveyNational41.22
      NAC-MICGuadeloupeDiabetes Res Clin Pract. 1991 Jul;12(3):209–1619851036FBGPeer-reviewedNational18.52
      NAC-MICJamaicaWest Indian Med J. 2011 Jul;60(4):422–820082848FBGPeer-reviewedNational25
      NAC-LICHaitiDiabetes Metab. 2006 Nov;32(5 Pt 1):443–5120031113OGTTPeer-reviewedRegional29.4
      SACA-MICArgentinaDiabet Med. 2009 Sep;26(9):864–7120051482FBGPeer-reviewedRegional20
      SACA-MICBoliviaRev Panam Salud Publica. 2001 Nov;10(5):318–2319982533OGTTPeer-reviewedRegional27.78
      SACA-MICChileEncuesta nacional de salud Chile, 201020105000FBGNational Health SurveyNational21.51
      SACA-MICColombiaWHO STEPS Santander, 201120101575FBGNational Health SurveyRegional53
      SACA-MICCosta RicaCAMDI Report Costa Rica, 200420041427OGTTNational Health SurveyRegional24.05
      SACA-MICEcuadorDiabet Med. 2009 Sep;26(9):864–7120051638FBGPeer-reviewedRegional20
      SACA-MICGuatemalaCAMDI Report Guatemala, 200720061397OGTTNational Health SurveyRegional48.81
      SACA-MICHondurasCAMDI Report Honduras, 200920041696OGTTNational Health SurveyLocal50
      SACA-MICNicaraguaCAMDI Report Nicaragua, 201020091993OGTTNational Health SurveyLocal43.33
      SACA-MICPeruDiabet Med. 2009 Sep;26(9):864–7120051652FBGPeer-reviewedRegional20
      SACA-MICVenezuelaDiabet Med. 2009 Sep;26(9):864–7120051848FBGPeer-reviewedRegional20
      SEA-MICBhutanWHO STEPS Bhutan, 200920072464OGTTNational Health SurveyLocal69.51
      SEA-MICIndiaDiabetologia. 2011 Dec;54(12):3022–7201013 050OGTTPeer-reviewedNational53.1
      SEA-MICMauritiusDiabet Med. 2005 Jan;22(1):61–819985566OGTTPeer-reviewedNational49.05
      SEA-MICMauritiusWHO STEPS Mauritius, 200620044200OGTTNational Health SurveyNational46.6
      SEA-MICSri LankaDiabet Med. 2008 Sep;25(9):1062–920064532OGTTPeer-reviewedNational35.78
      SEA-LICNepalSoutheast Asian J Trop Med Public Health. 2011 Jan;42(1):197–20720062011FBGPeer-reviewedRegional43.6
      WP-HICHong Kong ChinaDiabet Med. 2000 Nov;17(11):798–80619962664OGTTPeer-reviewedNational64.43
      WP-HICRepublic of KoreaDiabetes Care. 2006 Feb;29(2):226–3120015844FBGPeer-reviewedNational43.42
      WP-HICRepublic of KoreaDiabetes Care. 2009 Nov;32(11):2016–20. Epub 2009 Aug 1220054628FBGPeer-reviewedNational31.87
      WP-HICSingaporeDiabetes Care. 1999 Feb;22(2):241–719923568OGTTPeer-reviewedNational58.5
      WP-HICSingaporeNational Health Surveillance Survey 200420044168OGTTNational Health SurveyNational49.4
      WP-MICChinaN Engl J Med. 2010 Mar 25;362(12):1090–101200846 239OGTTPeer-reviewedNational58.76
      WP-MICChinaDiabetes Care. 2012 May;35(5):1028–30. doi: 10.2337/dc11-1212. Epub 2012 Mar 1920097964OGTTPeer-reviewedRegional41.3
      WP-MICFijiWHO STEPS Fiji, 200220022277FBGNational Health SurveyNational53.2
      WP-MICIndonesiaActa Med Indones. 2009 Oct;41(4):169–74200724 417OGTTPeer-reviewedNational73.68
      WP-MICMalaysiaAsia Pac J Public Health. 2010 Apr;22(2):194–202. Epub 2009 May 1420047683FBGPeer-reviewedNational55
      WP-MICMalaysiaWHO STEPS Malaysia, 200620063040FBGNational Health SurveyNational59.1
      WP-MICMalaysiaMed J Malaysia. 2010;65(3)200634 539FBGPeer-reviewedNational39.6
      WP-MICMongoliaWHO STEPS Mongolia, 200720063411FBGNational Health SurveyNational78.51
      WP-MICNauruBMC Public Health. 2011 Sep 23;11:71920041592FBGPeer-reviewedNational48.1
      WP-MICSamoaDiabetes Care. 1994 Apr;17(4):288–9619911776OGTTPeer-reviewedNational47.98
      WP-MICThailandDiabetes Care. 2011 Sep;34(9):1980–5200918 629FBGPeer-reviewedNational27.4
      WP-MICTongaDiabetes Care. 2002 Aug;25(8):1378–8320001024OGTTPeer-reviewedNational80
      WP-LICCambodiaWHO STEPS Cambodia, 201020095123FBGNational Health SurveyNational63.04

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